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Biomedicines
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Applications of Mass Spectrometry in Biomedical Research
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Applications of Mass Spectrometry in Biomedical Research

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 949

Special Issue Editor

Dr. Robin Joshi

E-Mail Website
Guest Editor
Perelman School of Medicine, ITMAT, University of Pennsylvania, Philadelphia, PA, USA
Interests: metabolomics; proteomics; lipidomics; mass spectrometry; circadian biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mass spectrometry (MS) has now become an indispensable analytical platform in biomedical research due to its high sensitivity, specificity, and ability to perform comprehensive molecular and biomolecular profiling. It enables the qualitative and quantitative analysis of a wide range of biomolecules, such as metabolites, lipids, proteins, and drugs, in various complex biological matrices.

In proteomics, MS is widely employed to identify and quantify proteins, characterize post-translational modifications, and map signaling pathways involved in health and disease. In metabolomics and lipidomics, MS helps uncover the metabolic alterations associated with conditions such as cardiovascular disease, cancer, diabetes, and neurodegeneration, providing insights into disease mechanisms and potential biomarkers.

MS also plays a crucial role in clinical and translational research, including biomarker investigations, therapeutic drug monitoring, and pharmacokinetics. Advanced approaches such as imaging mass spectrometry (e.g., MALDI, DESI) allow spatial mapping of metabolites and lipids directly in tissues, linking molecular changes to histological features and improving our understanding of disease microenvironments. With ongoing advancements in instrumentation, ionization techniques, and bioinformatics, mass spectrometry continues to drive precision medicine by enabling systems-level molecular characterization and supporting the development of targeted diagnostic tools.

Dr. Robin Joshi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • multiomics
  • method development
  • mass spectrometry
  • proteomics and post-translational modifications in disease
  • translational medicines

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Published Papers (1 paper)

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Research

18 pages, 5095 KB  
Article
Evaluation of MassFrontier, MetFrag, MS-FINDER, and SIRIUS for Metabolite Annotation Using an Experimental LC–HRMS Dataset
by Dmitrii A. Leonov, Irina A. Mednova and Alexander A. Chernonosov
Biomedicines 2026, 14(4), 872; https://doi.org/10.3390/biomedicines14040872 - 10 Apr 2026
Viewed by 737
Abstract
Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains [...] Read more.
Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation. Full article
(This article belongs to the Special Issue Applications of Mass Spectrometry in Biomedical Research)
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